Facial Kinship Verification is the task of determining the degree of familial relationship between two facial images. It has recently gained a lot of interest in various applications spanning forensic science, social media, and demographic studies. In the past decade, deep learning-based approaches have emerged as a promising solution to this problem, achieving state-of-the-art performance. In this paper, we propose a novel method for solving kinship verification by using supervised contrastive learning, which trains the model to maximize the similarity between related individuals and minimize it between unrelated individuals. Our experiments show state-of-the-art results and achieve 81.1% accuracy in the Families in the Wild (FIW) dataset.

翻译：面部亲属关系验证是一项确定两张面部图像之间家庭关系程度的任务。近年来，该任务在法医学、社交媒体和人口统计学研究等多个应用领域引起了广泛关注。过去十年间，基于深度学习的方法已成为解决该问题的有前景方案，并取得了最先进的性能。本文提出了一种利用监督对比学习解决亲属关系验证的新方法，该方法通过训练模型使相关个体间的相似度最大化，非相关个体间的相似度最小化。我们的实验在野生家庭（Families in the Wild, FIW）数据集上取得了最先进的结果，准确率达到81.1%。